Recipe texts are an idiosyncratic form of instructional language that pose unique challenges for automatic understanding. One challenge is that a cooking step in one recipe can be explained in another recipe in different words, at a different level of abstraction, or not at all. Previous work has annotated correspondences between recipe instructions at the sentence level, often glossing over important correspondences between cooking steps across recipes. We present a novel and fully-parsed English recipe corpus, ARA (Aligned Recipe Actions), which annotates correspondences between individual actions across similar recipes with the goal of capturing information implicit for accurate recipe understanding. We represent this information in the form of recipe graphs, and we train a neural model for predicting correspondences on ARA. We find that substantial gains in accuracy can be obtained by taking fine-grained structural information about the recipes into account.
Lucia Donatelli, Theresa Schmidt, Debanjali Biswas, Arne Köhn, Fangzhou Zhai, and Alexander Koller. 2021.Aligning Actions Across Recipe Graphs. InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6930–6942, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
@inproceedings{donatelli-etal-2021-aligning, title = "Aligning Actions Across Recipe Graphs", author = {Donatelli, Lucia and Schmidt, Theresa and Biswas, Debanjali and K{\"o}hn, Arne and Zhai, Fangzhou and Koller, Alexander}, editor = "Moens, Marie-Francine and Huang, Xuanjing and Specia, Lucia and Yih, Scott Wen-tau", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.554/", doi = "10.18653/v1/2021.emnlp-main.554", pages = "6930--6942", abstract = "Recipe texts are an idiosyncratic form of instructional language that pose unique challenges for automatic understanding. One challenge is that a cooking step in one recipe can be explained in another recipe in different words, at a different level of abstraction, or not at all. Previous work has annotated correspondences between recipe instructions at the sentence level, often glossing over important correspondences between cooking steps across recipes. We present a novel and fully-parsed English recipe corpus, ARA (Aligned Recipe Actions), which annotates correspondences between individual actions across similar recipes with the goal of capturing information implicit for accurate recipe understanding. We represent this information in the form of recipe graphs, and we train a neural model for predicting correspondences on ARA. We find that substantial gains in accuracy can be obtained by taking fine-grained structural information about the recipes into account."}
<?xml version="1.0" encoding="UTF-8"?><modsCollection xmlns="http://www.loc.gov/mods/v3"><mods ID="donatelli-etal-2021-aligning"> <titleInfo> <title>Aligning Actions Across Recipe Graphs</title> </titleInfo> <name type="personal"> <namePart type="given">Lucia</namePart> <namePart type="family">Donatelli</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Theresa</namePart> <namePart type="family">Schmidt</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Debanjali</namePart> <namePart type="family">Biswas</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Arne</namePart> <namePart type="family">Köhn</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Fangzhou</namePart> <namePart type="family">Zhai</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Alexander</namePart> <namePart type="family">Koller</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <originInfo> <dateIssued>2021-11</dateIssued> </originInfo> <typeOfResource>text</typeOfResource> <relatedItem type="host"> <titleInfo> <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title> </titleInfo> <name type="personal"> <namePart type="given">Marie-Francine</namePart> <namePart type="family">Moens</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Xuanjing</namePart> <namePart type="family">Huang</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Lucia</namePart> <namePart type="family">Specia</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Scott</namePart> <namePart type="given">Wen-tau</namePart> <namePart type="family">Yih</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <originInfo> <publisher>Association for Computational Linguistics</publisher> <place> <placeTerm type="text">Online and Punta Cana, Dominican Republic</placeTerm> </place> </originInfo> <genre authority="marcgt">conference publication</genre> </relatedItem> <abstract>Recipe texts are an idiosyncratic form of instructional language that pose unique challenges for automatic understanding. One challenge is that a cooking step in one recipe can be explained in another recipe in different words, at a different level of abstraction, or not at all. Previous work has annotated correspondences between recipe instructions at the sentence level, often glossing over important correspondences between cooking steps across recipes. We present a novel and fully-parsed English recipe corpus, ARA (Aligned Recipe Actions), which annotates correspondences between individual actions across similar recipes with the goal of capturing information implicit for accurate recipe understanding. We represent this information in the form of recipe graphs, and we train a neural model for predicting correspondences on ARA. We find that substantial gains in accuracy can be obtained by taking fine-grained structural information about the recipes into account.</abstract> <identifier type="citekey">donatelli-etal-2021-aligning</identifier> <identifier type="doi">10.18653/v1/2021.emnlp-main.554</identifier> <location> <url>https://aclanthology.org/2021.emnlp-main.554/</url> </location> <part> <date>2021-11</date> <extent unit="page"> <start>6930</start> <end>6942</end> </extent> </part></mods></modsCollection>
%0 Conference Proceedings%T Aligning Actions Across Recipe Graphs%A Donatelli, Lucia%A Schmidt, Theresa%A Biswas, Debanjali%A Köhn, Arne%A Zhai, Fangzhou%A Koller, Alexander%Y Moens, Marie-Francine%Y Huang, Xuanjing%Y Specia, Lucia%Y Yih, Scott Wen-tau%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing%D 2021%8 November%I Association for Computational Linguistics%C Online and Punta Cana, Dominican Republic%F donatelli-etal-2021-aligning%X Recipe texts are an idiosyncratic form of instructional language that pose unique challenges for automatic understanding. One challenge is that a cooking step in one recipe can be explained in another recipe in different words, at a different level of abstraction, or not at all. Previous work has annotated correspondences between recipe instructions at the sentence level, often glossing over important correspondences between cooking steps across recipes. We present a novel and fully-parsed English recipe corpus, ARA (Aligned Recipe Actions), which annotates correspondences between individual actions across similar recipes with the goal of capturing information implicit for accurate recipe understanding. We represent this information in the form of recipe graphs, and we train a neural model for predicting correspondences on ARA. We find that substantial gains in accuracy can be obtained by taking fine-grained structural information about the recipes into account.%R 10.18653/v1/2021.emnlp-main.554%U https://aclanthology.org/2021.emnlp-main.554/%U https://doi.org/10.18653/v1/2021.emnlp-main.554%P 6930-6942
Lucia Donatelli, Theresa Schmidt, Debanjali Biswas, Arne Köhn, Fangzhou Zhai, and Alexander Koller. 2021.Aligning Actions Across Recipe Graphs. InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6930–6942, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.